{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "\n",
    "import os\n",
    "import torch\n",
    "import torchvision\n",
    "import os\n",
    "from data_loader import show_images\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "5469\n"
     ]
    }
   ],
   "source": [
    "from torchvision import transforms\n",
    "from PIL import Image\n",
    "\n",
    "root_dir='/home/jinyue/work/pcy241003/data/edeme/'\n",
    "transform = transforms.Compose([\n",
    "  transforms.Grayscale(num_output_channels=1),  # 转换为单色图片（1通道）\n",
    "    transforms.Resize((512, 512)),                # 调整大小为 512x512\n",
    "    transforms.ToTensor()                         # 转换为 PyTorch Tensor\n",
    "    ])\n",
    "\n",
    "def read_imagesb(root_dir, is_train=True):\n",
    "    features = []\n",
    "    labels = []\n",
    "    for i, fname in enumerate(os.listdir(os.path.join(root_dir, 'label'))):\n",
    "        if i >= 100 and 0:\n",
    "            break\n",
    "        lab_path = os.path.join(root_dir, 'label', fname)\n",
    "        pic_path = os.path.join(root_dir, 'pic', fname)\n",
    "        if os.path.isfile(lab_path) and os.path.isfile(pic_path):\n",
    "            feature_image = Image.open(pic_path)\n",
    "            label_image = Image.open(lab_path)\n",
    "            # 对 feature 和 label 应用相同的转换\n",
    "            features.append(transform(feature_image))\n",
    "            labels.append(transform(label_image))\n",
    "    print(i)\n",
    "    return features, labels\n",
    "def read_images(root_dir, is_train=True):\n",
    "    features = []\n",
    "    labels = []\n",
    "    for i, fname in enumerate(os.listdir(os.path.join(root_dir, 'label'))):\n",
    "        if i >= 100 and 0:\n",
    "            break\n",
    "        lab_path = os.path.join(root_dir, 'label', fname)\n",
    "        pic_path = os.path.join(root_dir, 'pic', fname)\n",
    "        if os.path.isfile(lab_path) and os.path.isfile(pic_path):\n",
    "            feature_image = torchvision.io.read_image(pic_path)\n",
    "            label_image = torchvision.io.read_image(lab_path)\n",
    "            # 对 feature 和 label 应用相同的转换\n",
    "            features.append((feature_image))\n",
    "            labels.append((label_image))\n",
    "    print(i)\n",
    "    return features, labels\n",
    "\n",
    "features, labels = read_images(root_dir, True)\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "from torch.utils.data import random_split\n",
    "\n",
    "\n",
    "# 转为 TensorDataset\n",
    "dataset = torch.utils.data.TensorDataset(torch.stack(features), torch.stack(labels))\n",
    "# 假设 dataset 是你加载的完整数据集\n",
    "total_size = len(dataset)  # 数据集的总大小\n",
    "\n",
    "# 设置划分比例\n",
    "train_ratio ,val_ratio,test_ratio= 0.6 ,0.2,0.2 # 60% 用于训练\n",
    "\n",
    "# 计算每个子集的大小\n",
    "train_size = int(train_ratio * total_size)\n",
    "val_size = int(val_ratio * total_size)\n",
    "test_size = total_size - train_size - val_size  # 确保总大小正确\n",
    "# 随机划分\n",
    "train_set, val_set, test_set = random_split(dataset, [train_size, val_size, test_size])\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 300x750 with 10 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "imgs = []\n",
    "n = 5\n",
    "for i in range(n):\n",
    "    # 从 dataset 中取出单个样本 (features, labels)\n",
    "    f, l = test_set[i]\n",
    "\n",
    "    # 将 feature 和 label 都转换为 NumPy 数组并追加到 imgs 列表中\n",
    "    img_feature = f.numpy().transpose(1, 2, 0)\n",
    "    img_label = l.numpy().transpose(1, 2, 0)\n",
    "    # 将处理后的 feature 和 label 添加到 imgs 中\n",
    "    imgs.append(img_feature)\n",
    "    imgs.append(img_label)\n",
    "\n",
    "\n",
    "show_images(imgs, n, 2);"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/media/disk/Backup/01drive/07jinyue/miniconda3/envs/pcy/lib/python3.9/site-packages/torchvision/models/_utils.py:208: UserWarning: The parameter 'pretrained' is deprecated since 0.13 and may be removed in the future, please use 'weights' instead.\n",
      "  warnings.warn(\n",
      "/media/disk/Backup/01drive/07jinyue/miniconda3/envs/pcy/lib/python3.9/site-packages/torchvision/models/_utils.py:223: UserWarning: Arguments other than a weight enum or `None` for 'weights' are deprecated since 0.13 and may be removed in the future. The current behavior is equivalent to passing `weights=ResNet50_Weights.IMAGENET1K_V1`. You can also use `weights=ResNet50_Weights.DEFAULT` to get the most up-to-date weights.\n",
      "  warnings.warn(msg)\n"
     ]
    },
    {
     "ename": "RuntimeError",
     "evalue": "Given groups=1, weight of size [64, 3, 7, 7], expected input[1, 1, 512, 512] to have 3 channels, but got 1 channels instead",
     "output_type": "error",
     "traceback": [
      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[0;31mRuntimeError\u001b[0m                              Traceback (most recent call last)",
      "Cell \u001b[0;32mIn[5], line 26\u001b[0m\n\u001b[1;32m     24\u001b[0m images, labels \u001b[38;5;241m=\u001b[39m images\u001b[38;5;241m.\u001b[39mto(device), labels\u001b[38;5;241m.\u001b[39mto(device)\n\u001b[1;32m     25\u001b[0m \u001b[38;5;66;03m# 前向传播\u001b[39;00m\n\u001b[0;32m---> 26\u001b[0m outputs \u001b[38;5;241m=\u001b[39m \u001b[43mmodel\u001b[49m\u001b[43m(\u001b[49m\u001b[43mimages\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m     27\u001b[0m loss \u001b[38;5;241m=\u001b[39m criterion(outputs, labels)\n\u001b[1;32m     29\u001b[0m \u001b[38;5;66;03m# 反向传播和优化\u001b[39;00m\n",
      "File \u001b[0;32m/media/disk/Backup/01drive/07jinyue/miniconda3/envs/pcy/lib/python3.9/site-packages/torch/nn/modules/module.py:1553\u001b[0m, in \u001b[0;36mModule._wrapped_call_impl\u001b[0;34m(self, *args, **kwargs)\u001b[0m\n\u001b[1;32m   1551\u001b[0m     \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_compiled_call_impl(\u001b[38;5;241m*\u001b[39margs, \u001b[38;5;241m*\u001b[39m\u001b[38;5;241m*\u001b[39mkwargs)  \u001b[38;5;66;03m# type: ignore[misc]\u001b[39;00m\n\u001b[1;32m   1552\u001b[0m \u001b[38;5;28;01melse\u001b[39;00m:\n\u001b[0;32m-> 1553\u001b[0m     \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43m_call_impl\u001b[49m\u001b[43m(\u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43margs\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43mkwargs\u001b[49m\u001b[43m)\u001b[49m\n",
      "File \u001b[0;32m/media/disk/Backup/01drive/07jinyue/miniconda3/envs/pcy/lib/python3.9/site-packages/torch/nn/modules/module.py:1562\u001b[0m, in \u001b[0;36mModule._call_impl\u001b[0;34m(self, *args, **kwargs)\u001b[0m\n\u001b[1;32m   1557\u001b[0m \u001b[38;5;66;03m# If we don't have any hooks, we want to skip the rest of the logic in\u001b[39;00m\n\u001b[1;32m   1558\u001b[0m \u001b[38;5;66;03m# this function, and just call forward.\u001b[39;00m\n\u001b[1;32m   1559\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m \u001b[38;5;129;01mnot\u001b[39;00m (\u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_backward_hooks \u001b[38;5;129;01mor\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_backward_pre_hooks \u001b[38;5;129;01mor\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_forward_hooks \u001b[38;5;129;01mor\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_forward_pre_hooks\n\u001b[1;32m   1560\u001b[0m         \u001b[38;5;129;01mor\u001b[39;00m _global_backward_pre_hooks \u001b[38;5;129;01mor\u001b[39;00m _global_backward_hooks\n\u001b[1;32m   1561\u001b[0m         \u001b[38;5;129;01mor\u001b[39;00m _global_forward_hooks \u001b[38;5;129;01mor\u001b[39;00m _global_forward_pre_hooks):\n\u001b[0;32m-> 1562\u001b[0m     \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[43mforward_call\u001b[49m\u001b[43m(\u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43margs\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43mkwargs\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m   1564\u001b[0m \u001b[38;5;28;01mtry\u001b[39;00m:\n\u001b[1;32m   1565\u001b[0m     result \u001b[38;5;241m=\u001b[39m \u001b[38;5;28;01mNone\u001b[39;00m\n",
      "File \u001b[0;32m/media/disk/Backup/01drive/07jinyue/pcy241003/UNetResNet50.py:29\u001b[0m, in \u001b[0;36mUNetResNet50.forward\u001b[0;34m(self, x)\u001b[0m\n\u001b[1;32m     28\u001b[0m \u001b[38;5;28;01mdef\u001b[39;00m \u001b[38;5;21mforward\u001b[39m(\u001b[38;5;28mself\u001b[39m, x):\n\u001b[0;32m---> 29\u001b[0m     x \u001b[38;5;241m=\u001b[39m \u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mencoder\u001b[49m\u001b[43m(\u001b[49m\u001b[43mx\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m     30\u001b[0m     x \u001b[38;5;241m=\u001b[39m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mdecoder(x)\n\u001b[1;32m     31\u001b[0m     \u001b[38;5;28;01mreturn\u001b[39;00m x\n",
      "File \u001b[0;32m/media/disk/Backup/01drive/07jinyue/miniconda3/envs/pcy/lib/python3.9/site-packages/torch/nn/modules/module.py:1553\u001b[0m, in \u001b[0;36mModule._wrapped_call_impl\u001b[0;34m(self, *args, **kwargs)\u001b[0m\n\u001b[1;32m   1551\u001b[0m     \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_compiled_call_impl(\u001b[38;5;241m*\u001b[39margs, \u001b[38;5;241m*\u001b[39m\u001b[38;5;241m*\u001b[39mkwargs)  \u001b[38;5;66;03m# type: ignore[misc]\u001b[39;00m\n\u001b[1;32m   1552\u001b[0m \u001b[38;5;28;01melse\u001b[39;00m:\n\u001b[0;32m-> 1553\u001b[0m     \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43m_call_impl\u001b[49m\u001b[43m(\u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43margs\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43mkwargs\u001b[49m\u001b[43m)\u001b[49m\n",
      "File \u001b[0;32m/media/disk/Backup/01drive/07jinyue/miniconda3/envs/pcy/lib/python3.9/site-packages/torch/nn/modules/module.py:1562\u001b[0m, in \u001b[0;36mModule._call_impl\u001b[0;34m(self, *args, **kwargs)\u001b[0m\n\u001b[1;32m   1557\u001b[0m \u001b[38;5;66;03m# If we don't have any hooks, we want to skip the rest of the logic in\u001b[39;00m\n\u001b[1;32m   1558\u001b[0m \u001b[38;5;66;03m# this function, and just call forward.\u001b[39;00m\n\u001b[1;32m   1559\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m \u001b[38;5;129;01mnot\u001b[39;00m (\u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_backward_hooks \u001b[38;5;129;01mor\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_backward_pre_hooks \u001b[38;5;129;01mor\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_forward_hooks \u001b[38;5;129;01mor\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_forward_pre_hooks\n\u001b[1;32m   1560\u001b[0m         \u001b[38;5;129;01mor\u001b[39;00m _global_backward_pre_hooks \u001b[38;5;129;01mor\u001b[39;00m _global_backward_hooks\n\u001b[1;32m   1561\u001b[0m         \u001b[38;5;129;01mor\u001b[39;00m _global_forward_hooks \u001b[38;5;129;01mor\u001b[39;00m _global_forward_pre_hooks):\n\u001b[0;32m-> 1562\u001b[0m     \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[43mforward_call\u001b[49m\u001b[43m(\u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43margs\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43mkwargs\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m   1564\u001b[0m \u001b[38;5;28;01mtry\u001b[39;00m:\n\u001b[1;32m   1565\u001b[0m     result \u001b[38;5;241m=\u001b[39m \u001b[38;5;28;01mNone\u001b[39;00m\n",
      "File \u001b[0;32m/media/disk/Backup/01drive/07jinyue/miniconda3/envs/pcy/lib/python3.9/site-packages/torch/nn/modules/container.py:219\u001b[0m, in \u001b[0;36mSequential.forward\u001b[0;34m(self, input)\u001b[0m\n\u001b[1;32m    217\u001b[0m \u001b[38;5;28;01mdef\u001b[39;00m \u001b[38;5;21mforward\u001b[39m(\u001b[38;5;28mself\u001b[39m, \u001b[38;5;28minput\u001b[39m):\n\u001b[1;32m    218\u001b[0m     \u001b[38;5;28;01mfor\u001b[39;00m module \u001b[38;5;129;01min\u001b[39;00m \u001b[38;5;28mself\u001b[39m:\n\u001b[0;32m--> 219\u001b[0m         \u001b[38;5;28minput\u001b[39m \u001b[38;5;241m=\u001b[39m \u001b[43mmodule\u001b[49m\u001b[43m(\u001b[49m\u001b[38;5;28;43minput\u001b[39;49m\u001b[43m)\u001b[49m\n\u001b[1;32m    220\u001b[0m     \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[38;5;28minput\u001b[39m\n",
      "File \u001b[0;32m/media/disk/Backup/01drive/07jinyue/miniconda3/envs/pcy/lib/python3.9/site-packages/torch/nn/modules/module.py:1553\u001b[0m, in \u001b[0;36mModule._wrapped_call_impl\u001b[0;34m(self, *args, **kwargs)\u001b[0m\n\u001b[1;32m   1551\u001b[0m     \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_compiled_call_impl(\u001b[38;5;241m*\u001b[39margs, \u001b[38;5;241m*\u001b[39m\u001b[38;5;241m*\u001b[39mkwargs)  \u001b[38;5;66;03m# type: ignore[misc]\u001b[39;00m\n\u001b[1;32m   1552\u001b[0m \u001b[38;5;28;01melse\u001b[39;00m:\n\u001b[0;32m-> 1553\u001b[0m     \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43m_call_impl\u001b[49m\u001b[43m(\u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43margs\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43mkwargs\u001b[49m\u001b[43m)\u001b[49m\n",
      "File \u001b[0;32m/media/disk/Backup/01drive/07jinyue/miniconda3/envs/pcy/lib/python3.9/site-packages/torch/nn/modules/module.py:1562\u001b[0m, in \u001b[0;36mModule._call_impl\u001b[0;34m(self, *args, **kwargs)\u001b[0m\n\u001b[1;32m   1557\u001b[0m \u001b[38;5;66;03m# If we don't have any hooks, we want to skip the rest of the logic in\u001b[39;00m\n\u001b[1;32m   1558\u001b[0m \u001b[38;5;66;03m# this function, and just call forward.\u001b[39;00m\n\u001b[1;32m   1559\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m \u001b[38;5;129;01mnot\u001b[39;00m (\u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_backward_hooks \u001b[38;5;129;01mor\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_backward_pre_hooks \u001b[38;5;129;01mor\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_forward_hooks \u001b[38;5;129;01mor\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_forward_pre_hooks\n\u001b[1;32m   1560\u001b[0m         \u001b[38;5;129;01mor\u001b[39;00m _global_backward_pre_hooks \u001b[38;5;129;01mor\u001b[39;00m _global_backward_hooks\n\u001b[1;32m   1561\u001b[0m         \u001b[38;5;129;01mor\u001b[39;00m _global_forward_hooks \u001b[38;5;129;01mor\u001b[39;00m _global_forward_pre_hooks):\n\u001b[0;32m-> 1562\u001b[0m     \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[43mforward_call\u001b[49m\u001b[43m(\u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43margs\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43mkwargs\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m   1564\u001b[0m \u001b[38;5;28;01mtry\u001b[39;00m:\n\u001b[1;32m   1565\u001b[0m     result \u001b[38;5;241m=\u001b[39m \u001b[38;5;28;01mNone\u001b[39;00m\n",
      "File \u001b[0;32m/media/disk/Backup/01drive/07jinyue/miniconda3/envs/pcy/lib/python3.9/site-packages/torch/nn/modules/conv.py:458\u001b[0m, in \u001b[0;36mConv2d.forward\u001b[0;34m(self, input)\u001b[0m\n\u001b[1;32m    457\u001b[0m \u001b[38;5;28;01mdef\u001b[39;00m \u001b[38;5;21mforward\u001b[39m(\u001b[38;5;28mself\u001b[39m, \u001b[38;5;28minput\u001b[39m: Tensor) \u001b[38;5;241m-\u001b[39m\u001b[38;5;241m>\u001b[39m Tensor:\n\u001b[0;32m--> 458\u001b[0m     \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43m_conv_forward\u001b[49m\u001b[43m(\u001b[49m\u001b[38;5;28;43minput\u001b[39;49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mweight\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mbias\u001b[49m\u001b[43m)\u001b[49m\n",
      "File \u001b[0;32m/media/disk/Backup/01drive/07jinyue/miniconda3/envs/pcy/lib/python3.9/site-packages/torch/nn/modules/conv.py:454\u001b[0m, in \u001b[0;36mConv2d._conv_forward\u001b[0;34m(self, input, weight, bias)\u001b[0m\n\u001b[1;32m    450\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mpadding_mode \u001b[38;5;241m!=\u001b[39m \u001b[38;5;124m'\u001b[39m\u001b[38;5;124mzeros\u001b[39m\u001b[38;5;124m'\u001b[39m:\n\u001b[1;32m    451\u001b[0m     \u001b[38;5;28;01mreturn\u001b[39;00m F\u001b[38;5;241m.\u001b[39mconv2d(F\u001b[38;5;241m.\u001b[39mpad(\u001b[38;5;28minput\u001b[39m, \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_reversed_padding_repeated_twice, mode\u001b[38;5;241m=\u001b[39m\u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mpadding_mode),\n\u001b[1;32m    452\u001b[0m                     weight, bias, \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mstride,\n\u001b[1;32m    453\u001b[0m                     _pair(\u001b[38;5;241m0\u001b[39m), \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mdilation, \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mgroups)\n\u001b[0;32m--> 454\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[43mF\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mconv2d\u001b[49m\u001b[43m(\u001b[49m\u001b[38;5;28;43minput\u001b[39;49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mweight\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mbias\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mstride\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m    455\u001b[0m \u001b[43m                \u001b[49m\u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mpadding\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mdilation\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mgroups\u001b[49m\u001b[43m)\u001b[49m\n",
      "\u001b[0;31mRuntimeError\u001b[0m: Given groups=1, weight of size [64, 3, 7, 7], expected input[1, 1, 512, 512] to have 3 channels, but got 1 channels instead"
     ]
    }
   ],
   "source": [
    "from UNetResNet50 import UNetResNet50\n",
    "# 定义UNet模型，使用ResNet-50作为骨干网络\n",
    "import torch.nn as nn\n",
    "import torch.optim as optim\n",
    "\n",
    "\n",
    "# 超参数设置\n",
    "epochs = 100\n",
    "learning_rate = 1e-5\n",
    "batch_size = 2**4  # 根据显存情况调整\n",
    "num_classes = 1  # 假设是二分类问题\n",
    "device = torch.device(\"cuda\" )\n",
    "\n",
    "# 初始化模型、损失函数和优化器\n",
    "# model = UNetResNet50(num_classes=num_classes).to(device)\n",
    "import segmentation_models_pytorch as smp\n",
    "\n",
    "model = smp.Unet(\n",
    "    encoder_name=\"resnet50\",        # choose encoder, e.g. mobilenet_v2 or efficientnet-b7\n",
    "    encoder_weights=\"imagenet\",     # use `imagenet` pre-trained weights for encoder initialization\n",
    "    in_channels=3,                  # model input channels (1 for gray-scale images, 3 for RGB, etc.)\n",
    "    classes=1,                      # model output channels (number of classes in your dataset)\n",
    ")\n",
    "criterion = nn.BCEWithLogitsLoss()  # 二分类问题\n",
    "optimizer = optim.Adam(model.parameters(), lr=learning_rate)\n",
    "\n",
    "# 训练过程\n",
    "for epoch in range(epochs):\n",
    "    model.train()\n",
    "    running_loss = 0.0\n",
    "    for images, labels in train_set:\n",
    "        images, labels = images.to(device), labels.to(device)\n",
    "        # 前向传播\n",
    "        outputs = model(images)\n",
    "        loss = criterion(outputs, labels)\n",
    "\n",
    "        # 反向传播和优化\n",
    "        optimizer.zero_grad()\n",
    "        loss.backward()\n",
    "        optimizer.step()\n",
    "\n",
    "        running_loss += loss.item()\n",
    "    print(f\"Epoch [{epoch+1}/{epochs}], Loss: {running_loss/len(train_set)}\")\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "\n",
    "    # 验证模型\n",
    "    model.eval()\n",
    "    with torch.no_grad():\n",
    "        val_loss = 0.0\n",
    "        for images, labels in val_set:\n",
    "            images, labels = images.to(device), labels.to(device)\n",
    "            outputs = model(images)\n",
    "            loss = criterion(outputs, labels)\n",
    "            val_loss += loss.item()\n",
    "\n",
    "        print(f\"Validation Loss: {val_loss/len(val_set)}\")\n",
    "\n",
    "# 测试集上的评估\n",
    "def evaluate_model(model, loader):\n",
    "    model.eval()\n",
    "    with torch.no_grad():\n",
    "        metrics = {\"PA\": 0, \"CPA\": 0, \"MPA\": 0, \"IoU\": 0, \"MIoU\": 0}\n",
    "        for images, labels in loader:\n",
    "            images, labels = images.to(device), labels.to(device)\n",
    "            outputs = model(images)\n",
    "            # 在这里计算PA、CPA、MPA、IoU和MIoU等指标\n",
    "            # 可以根据实际需要定义你自己的评估函数\n",
    "        return metrics\n",
    "\n",
    "test_metrics = evaluate_model(model, test_set)\n",
    "print(f\"Test Metrics: {test_metrics}\")\n"
   ]
  }
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